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Track 2 (Baroja)
Track 3 (Oteiza)
Track4 (Chillida)
09:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
Getting Started with JupyterLab (Mike Müller)
Never get in a battle of bits without ammunition (Valerio Maggio)
Introduction to pandas (Marc Garcia)
Hands-on TensorFlow 2.0 (Josh Gordon)
Deep Diving into GANs: From Theory to Production with TensorFlow 2.0 (Michele "Ubik" De Simoni, Paolo Galeone, Federico Di Mattia, Emanuele Ghelfi)
Create CUDA kernels from Python using Numba and CuPy. (Valentin Haenel)
Speed up your python code (Jérémie du Boisberranger)
Reproducible Data Science in Python (Chandrasekhar Ramakrishnan, Rok Roškar)
Building data pipelines in Python: Airflow vs scripts soup (Dr. Tania Allard)
Performing Quantum Measurements in QuTiP (Simon Cross)
Track 2 (Baroja)
Track 3 (Oteiza)
Track4 (Chillida)
09:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
Introduction to SciPy (Gert-Ludwig Ingold)
A Tour of the Data Visualization Ecosystem of Python (Giovanni De Gasperis)
Introduction to scikit-learn: from model fitting to model interpretation (Guillaume Lemaitre, Olivier Grisel)
Sufficiently Advanced Testing with Hypothesis (Zac Hatfield-Dodds)
Effectively using matplotlib (Tim Hoffmann)
CFFI, Ctypes, Cython, Cppyy: how to run C code from Python (Matti Picus)
kCSD - a Python package for reconstruction of brain activity (Marta Kowalska, Jakub M. Dzik)
Introduction to geospatial data analysis with GeoPandas and the PyData stack (Joris Van den Bossche)
Astronomical Image Processing (Samuel FARRENS)
Parallelizing Python applications with PyCOMPSs (Javier Conejero)
Track 1 (Mitxelena)
Track 2 (Baroja)
Track 3 (Oteiza)
Posters at 16:00
08:00
09:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
18:00
19:00
From Galaxies to Brains! - Image processing with Python (Samuel FARRENS)
Distributed GPU Computing with Dask (Peter Andreas Entschev)
Modern Data Science: A new approach to DataFrames and pipelines (Jovan Veljanoski, Maarten Breddels)
Apache Arrow: a cross-language development platform for in-memory data (Joris Van den Bossche)
Caterva: A Compressed And Multidimensional Container For Big Data (Francesc Alted)
Modin: Scaling the Capabilities of the Data Scientist, not the machine (Devin Petersohn, Devin Petersohn)
Best Coding Practices in Jupyterlab (Alexander CS Hendorf)
Lessons learned from comparing Numba-CUDA and C-CUDA (Lena Oden)
How a voice assistant works (Miren Urteaga Aldalur)
QuTiP: the quantum toolbox in Python as an ecosystem for quantum physics exploration and quantum information science (Nathan Shammah, Alexander Pitchford)
Constrained Data Synthesis (Nick Radcliffe)
ToFu - an open-source python/cython library for synthetic tomography diagnostics on Tokamaks (Didier VEZINET, Laura Mendoza)
Environmental Research and Citizen Science using fractaL (Saulo Jacques)
Controlling a confounding effect in predictive analysis. (Darya Chyzhyk)
The Rapid Analytics and Model Prototyping (RAMP) framework: tools for collaborative data science challenges (Guillaume Lemaitre, Joris Van den Bossche)
Sufficiently Advanced Testing with Hypothesis (Zac Hatfield-Dodds)
What about tests in Machine Learning projects? (Sarah Diot-Girard)
Scientific DevOps: Designing Reproducible Data Analysis Pipelines with Containerized Workflow Managers (Nicholas Del Grosso)
Dashboarding with Jupyter notebooks, voila and widgets (Maarten Breddels, Martin Renou)
Make your Python code fly at transonic speeds! (Pierre Augier)
PyFETI - An easy and massively Dual Domain Decomposition Solver for Python (Guilherme Jenovencio)
High Voltage Lab Common Code Basis library: a uniform user-friendly object-oriented API for a high voltage engineering research. (Mikołaj Rybiński)
scikit-fdiff, a new tool for PDE solving (Nicolas Cellier)
PhonoLAMMPS: Phonopy with LAMMPS made easy (Abel Carreras)
Really reproducible behavioural paper (Jakub M. Dzik)
kESI - a kernel-based method for reconstruction of sources of brain electric activity in realistic brain geometries (Marta Kowalska, Jakub M. Dzik)
From Modeler to Programmer (Dr. Mike Müller)
MNE-Python, a toolkit for neurophysiological data (Joan Massich)
Track 1 (Mitxelena)
Track 2 (Baroja)
Track 3 (Oteiza)
09:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
HPC and Python: Intel’s work in enabling the scientific computing community (David Liu)
Inside NumPy: preparing for the next decade (Matti Picus)
Introduction to TensorFlow 2.0 (Brad Miro)
The Magic of Neural Embeddings with TensorFlow 2 (Oliver Zeigermann)
High quality video experience using deep neural networks (Marco Bertini, Tiberio Uricchio)
In the Shadow of the Black Hole (Sara Issaoun)
A practical guide towards algorithmic bias and explainability in machine learning (Alejandro Saucedo)
Tracking migration flows with geolocated Twitter data (Antònia Tugores)
Deep Learning for Understanding Human Multi-modal Behavior (Ricardo Manhães Savii)
How to process hyperspectral data from a prototype imager using Python (Matti Eskelinen)
Enhancing & re-designing the QGIS user interface – a deep dive (Sebastian M. Ernst)
Visual Diagnostics at Scale (Dr. Rebecca Bilbro)
Histogram-based Gradient Boosting in scikit-learn 0.21 (Olivier Grisel)
Recent advances in python parallel computing (Pierre Glaser)
Data sciences in a polyglot world with xtensor and xframe (Sylvain Corlay, Wolf Vollprecht)
Understanding Numba (Valentin Haenel)
PyPy meets SciPy (Ronan Lamy)
High performance machine learning with dislib (Javier Álvarez)
Can we make Python fast without sacrificing readability? numba for Astrodynamics (Juan Luis Cano Rodríguez)
PSYDAC: a parallel finite element solver with automatic code generation (Yaman Güçlü)
Get Started with Variational Inference using Python (Suriyadeepan Ramamoorthy)
Exceeding Classical: Probabilistic Data Structures in Data Intensive Applications (Andrii Gakhov)
Driving a 30m Radio Telescope with Python (Francesco Pierfederici)
Matrix calculus with SymPy (Francesco Bonazzi)
VeloxChem: Python meets quantum chemistry and HPC (Olav Vahtras)
emzed: a Python based framework for analysis of mass-spectrometry data (Uwe Schmitt)
vtext: fast text processing in Python using Rust (Roman Yurchak)
pystencils: Speeding up stencil computations on CPUs and GPUs (Martin Bauer)
TelApy a Python module to compute free surface flows and sediments transport in geosciences (yoann audouin)
No talks on Friday, Sept. 6, 2019.